Methods and Systems for Admission Control and Resource Availability Prediction Considering User Equipment (UE) Mobility

ABSTRACT

Predicting mobile station migration between geographical locations of a wireless network can be achieved using a migration probability database. The database can be generated based on statistical information relating to the wireless network, such as historical migration patterns and associated mobility information (e.g., velocities, bin location, etc.). The migration probability database consolidates the statistical information into mobility prediction functions for estimating migration probabilities/trajectories based on dynamically reported mobility parameters. By example, mobility prediction functions can compute a likelihood that a mobile station will migrate between geographic regions based on a velocity of the mobile station. Accurate mobility prediction may improve resource provisioning efficiency during admission control and path selection, and can also be used to dynamically adjust handover margins.

This patent application is a continuation of U.S. Non-Provisionalapplication Ser. No. 14/106,531 filed on Dec. 13, 2013 and entitled“Methods and Systems for Admission Control and Resource AvailabilityPrediction Considering User Equipment (UE) Mobility,” which claimspriority to U.S. Provisional Application No. 61,736,965, filed on Dec.13, 2012 and entitled “Method and System for Admission Control by aControl Entity Party Considering Mobility,” and U.S. ProvisionalApplication No. 61/737,579, filed on Dec. 14, 2012 and entitled “Methodsand Systems for Resource Availability Prediction via EffectiveBandwidth-Based Traffic Characterization Considering UE Mobility andWireless Channel Impairment,” all of which are incorporated by referenceherein as if reproduced in their entireties.

TECHNICAL FIELD

The present invention relates generally to wireless communications, andin particular embodiments, to methods and systems for admission controland resource availability prediction considering user equipment (UE)mobility.

BACKGROUND

In conventional wireless networks, radio resource allocation decisionsreact to (and therefore lag) real-time changes in the networkenvironment, such as fluctuations in traffic density and resourceutilization. For example, handovers from source access points (APs) totarget APs may be initiated only after a mobile station migrates intothe service area of the target AP. This latency may lead to in-efficientresource utilization (e.g., sub-optimal admission and/or resourceallocation decisions) in dynamic networks. For example, networks mayneed to maintain larger bandwidth reserves (e.g., handover margins,etc.) to allow for large fluctuations in traffic and/or throughputdemand. Accordingly, mechanisms for more efficient resource allocationare desired.

SUMMARY OF THE INVENTION

Technical advantages are generally achieved, by embodiments of thisdisclosure which describe methods and systems for admission control andresource availability prediction considering user equipment (UE)mobility.

In accordance with an embodiment, a method for mobility prediction isprovided. In this example, the method includes gathering statisticalinformation for mobile station migration in a wireless network, andbuilding a migration probability table for the wireless network inaccordance with the statistical information. The wireless networkincludes at least a first geographic region and a second geographicregion. The migration probability table specifies probabilities thatmobile stations will migrate from the first geographic region to thesecond geographic region over one or more fixed periods. Theprobabilities are specified in accordance with mobility parameters ofthe mobile stations, and the migration probability table is configuredto be used for resource provisioning in the wireless network. Anapparatus for performing this method is also provided.

In accordance with another embodiment, a method for projecting handoversin a wireless network is provided. In this example, the method includesobtaining mobility parameters corresponding to a mobile station duringan initial time interval, and estimating migration probabilityinformation in accordance with the mobility parameter and a migrationprobability table. The mobile station is located outside of a coveragearea of the wireless network during the initial time interval. Themigration probability information specifies a likelihood that the mobilestation will migrate into the coverage area during a sequence of one ormore time intervals following the initial time interval. The methodfurther includes adjusting a handover margin for the coverage area inaccordance with the migration probability information. An apparatus forperforming this method is also provided.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the present invention, and theadvantages thereof, reference is now made to the following descriptionstaken in conjunction with the accompanying drawing, in which:

FIG. 1 illustrates a diagram of an embodiment communications network;

FIGS. 2A-2B illustrate diagrams depicting a mobile station migrationscenario;

FIG. 3 illustrates a flowchart of an embodiment method for mobilityprediction;

FIG. 4A illustrates a diagram of another embodiment communicationsnetwork;

FIG. 4B illustrates a diagram of yet another embodiment communicationsnetwork;

FIG. 5A illustrates a flowchart of an embodiment method for estimatingresource utilization based on mobility prediction;

FIG. 5B illustrates a diagram of a technique for estimating resourceutilization;

FIG. 6 illustrates a protocol diagram of an embodiment communicationssequence for achieving mobility prediction;

FIG. 7 illustrates a diagram depicting estimated mobile stationmigration trajectories and resulting resource usage predictions;

FIG. 8 illustrates a diagram depicting migration probabilities formultiple cells over a sequence of periods;

FIG. 9 illustrates a flowchart of an embodiment method for adjustinghandover margins based on mobility predictions;

FIG. 10 illustrates block diagrams of high level techniques forpredicting resource availability;

FIG. 11 illustrates a diagram of an embodiment system for predictingresource availability;

FIG. 12 illustrates a diagram of another embodiment system forpredicting resource availability;

FIG. 13 illustrates a diagram depicting estimated resource requirementsfor a service flow along a projected migration trajectory;

FIG. 14 illustrates a diagram of another embodiment system forpredicting resource availability;

FIGS. 15-20 illustrate graphs of simulation results obtained fromembodiment migration predictions techniques;

FIG. 21 illustrates a flowchart of an embodiment method for computingresources needed for handover traffic;

FIG. 22 illustrates a flowchart of an embodiment method for updatingparameters of a spectral efficiency prediction function based onspectral efficiency feedback data;

FIGS. 23-24 illustrate graphs of simulation results obtained fromembodiment migration predictions techniques;

FIG. 25 illustrates a diagram of a bandwidth spectrum comprising ahandover margin;

FIG. 26 illustrates a diagram demonstrating how loading data isexchanged in a network configured for shadow clustering;

FIG. 27 illustrates a diagram of an embodiment technique for predictingfuture migration positions of a mobile station based on the mobilestation's velocity;

FIGS. 28A-28B illustrate diagrams of an embodiment migration probabilitytable and corresponding migration patterns;

FIG. 29 illustrates a table showing new allocation techniques fordifferent traffic and prediction scenarios;

FIG. 30 illustrates a state diagram for adjusting prediction functionparameters based on effective bandwidth;

FIG. 31 illustrates a graph of bandwidth aggregation;

FIG. 32 illustrates a graph of outage probabilities for differentnumbers of sources;

FIG. 33 illustrates a graph of allocated rates for different numbers ofsources;

FIG. 34 illustrates a diagram of an embodiment network configured forper-bin geographical area partitioning;

FIG. 35 illustrates a diagram of an embodiment network configured forper-zone geographical area partitioning;

FIG. 36 illustrates a graph of a simulated system capacity evaluation ona per-bin-per-access node basis;

FIG. 37 illustrates a graph of a simulated system capacity evaluation ona per-bin-per-access node basis;

FIG. 38 illustrates a graph of a simulated system capacity evaluation ona per-access node basis;

FIG. 39 illustrates a graph of a simulated system capacity evaluation ona per-bin basis;

FIG. 40 illustrates a diagram of an embodiment device for computinghandover margins;

FIG. 41 illustrates a diagram depicting different guard bandconfigurations for different levels of UE mobility;

FIG. 42 illustrates a diagram of an embodiment system for computingguard bands;

FIG. 43 illustrates a protocol diagram of an embodiment communicationssequence for partitioning coverage areas and adjusting guard bands;

FIG. 44 illustrates a protocol diagram of an embodiment communicationssequence for achieving mobility prediction;

FIG. 45 illustrates a block diagram of an embodiment communicationsdevice; and

FIG. 46 illustrates a block diagram of an embodiment processing system.

Corresponding numerals and symbols in the different figures generallyrefer to corresponding parts unless otherwise indicated. The figures aredrawn to clearly illustrate the relevant aspects of the embodiments andare not necessarily drawn to scale.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

The making and using of the presently preferred embodiments arediscussed in detail below. It should be appreciated, however, that thepresent invention provides many applicable inventive concepts that canbe embodied in a wide variety of specific contexts. The specificembodiments discussed are merely illustrative of specific ways to makeand use the invention, and do not limit the scope of the invention.

Disclosed herein are techniques for predicting mobile station migrationbased on mobile station mobility parameters. More specifically, aspectsof this disclosure construct migration probability databases based onstatistical information relating to a wireless network. The statisticalinformation may include historical traffic information, such as mobilestation migration patterns and associated mobility information (e.g.,velocities, bin location, etc.). The migration probability databaseconsolidates the statistical information into mobility predictionfunctions for estimating migration probabilities/trajectories based ondynamically reported mobility parameters. By way of example, mobilityprediction functions may compute a likelihood that a mobile station willmigrate between geographic regions based on a velocity of the mobilestation. Accurate mobility prediction may improve resource provisioningefficiency. For example, accurate mobility prediction can be used duringadmission control to estimate resource availability. As another example,accurate mobility prediction may allow handover margins to bedynamically adjusted with increased precision, thereby increasing thepool of available resources and decreasing blocked/dropped calls.Additionally, mobility prediction can be used to estimate spectralefficiency when network loading information is available. These andother aspects are explained in greater detail below.

FIG. 1 illustrates a network 100 for communicating data. The network 100comprises an access point (AP) no having a coverage area 101, aplurality of stations (STAs) 120, and a backhaul network 130. The AP nomay comprise any component capable of providing wireless access by,inter alia, establishing uplink (dashed line) and/or downlink (dottedline) connections with the STAs 120, such as a base station, an enhancedbase station (eNB), a femtocell, and other wirelessly enabled devices.The STAs 120 may comprise any component capable of establishing awireless connection with the AP 110. The backhaul network 130 may be anycomponent or collection of components that allow data to be exchangedbetween the AP no and a remote end (not shown). In some embodiments, thenetwork 100 may comprise various other wireless devices, such as relays,femtocells, etc.

In some situations, predicting future resource utilization/availabilitymay facilitate more efficient resource provisioning and/or allocationdecisions. FIGS. 2A-2B illustrates an exemplary situation showing howuser admission and/or resource allocation can benefit from accurateprediction of resource utilization/availability in a wireless network200. As shown, the network 200 includes an access point (AP) 210 whosecoverage area is split into two geographical areas, namely BIN A and BINB. FIG. 2A illustrates the network 200 at a first instance in time (T),during which the AP 210 is providing wireless access to a mobile station260 located in BINA via a radio interface 216. During that same timeinstance, the AP 210 receives a service request from a mobile station270 requesting wireless service in the BIN A at a second instance intime (T+Δt). Conventionally, the AP 210 would make anyadmission/resource-allocation decisions under the assumption that themobile station 260 will continue to access the network from BIN A at thesecond instance in time. For example, the AP 210 may decide thatservicing both of the mobile stations 260, 270 would overload BIN A, andconsequently may decide to reject the service request from mobilestation 270. However, as shown in FIG. 2B, the mobile station 260 maymigrate to from BIN A to BIN B during an interim period (Δt) between thefirst instance in time (T) and the second instance in time (T+Δt).Alternatively, the mobile station's 260 may conclude prior to the secondinstance in time (T+Δt), in which case the mobile station 260 may stopaccessing the wireless network altogether (or at least, in a reducedcapacity, e.g., idle paging, etc.). In either case, the AP's 210conventional assumption may prove inaccurate, and the AP's 210 decisionto reject the service request of the mobile station 270 may haveundesirable affects, e.g., reduce the overall spectral efficiency of thenetwork 200, etc.

Aspects of this disclosure provide techniques for predicting resourceavailability/utilization in a wireless network to allow for moreefficient admission and/or resource allocation decisions. Embodimenttechniques may predict a mobile station's future position based onmobile station mobility parameters, e.g., velocity, direction, etc.These predictions may allow network devices (e.g., APs, controllers,etc.) to make more intelligent admission/resource-allocation decisions.For example, embodiments of this disclosure may allow the AP 210 topredict that the mobile station 260 will migrate from BIN A to BIN Bduring the interim period (Δt) based on a mobility parameters associatedwith the mobile station 260 at the first instance in time (T). As aresult of this prediction, the AP 210 may project that BIN A will not beoverloaded during the second time instance (T+Δt), and consequently theAP 210 may device to approve the service request of the mobile station270 at the first time instance (T).

In some embodiment, prediction techniques build migration probabilitytables from statistical information related to historical mobile stationmigration. For instance, network operators may analyze historicaltraffic loading information/statistics to identify migration tendenciesin a given area as a function of mobility parameters. By way of example,an AP may determine a likelihood that a mobile station having a specificvelocity will migrate from one geographic region to another over a fixedtime period. This may be particularly advantageous in highly predictivemobility scenarios, e.g., mobile devices traveling along an interstate,etc.

FIG. 3 illustrates an embodiment method 300 for mobility prediction, asmay be performed by a network device (e.g., AP, scheduler, centralcontroller, central entity, etc.). As shown, the method 300 begins withstep 310, where the network device gathers statistical information for awireless network. The statistical information may include bin-basedstatistical information relating to loading and/or traffic patterns inthe network, and may be generated based on real-time network operation,network simulations, or combinations thereof. Thereafter, the method 300proceeds to step 320, where the network device builds a migrationprobability table based on the statistical information. The migrationprobability table may associate mobility parameters (e.g., velocities,bin-location, etc.) with migration probabilities. Migrationprobabilities may indicate the likelihood that a mobile station having acertain mobility parameter (or set of parameters) will travel from onegeographic location to another over a fixed period of time. Next, themethod 300 proceeds to step 330, where the network device obtainscurrent mobility parameters for mobile stations in the wireless network.The mobility parameters may be associated with a specific instance orperiod in time, such as a first interval. Subsequently, the method 300proceeds to step 340, where the network device estimates migrationprobability information for the mobile stations based on the mobilityparameters and the migration probability table. In one embodiment, themigration probability information may include a probability that a givenmobile station will migrate from one bin to another during an interimperiod between the first time interval and a second time interval. Inanother embodiment, the migration probability information may include aset of probabilities that indicate likelihoods that a given mobilestation will migrate from one bin to each of a plurality of neighboringbins over the course of one or more fixed time periods. For example, theset of probabilities may include a first probability (or sub-set ofprobabilities) that the given mobile station will migrate from BIN A toBIN B over a period (or set of periods), a second probability (orsub-set of probabilities) that the given mobile station will migratefrom BIN A to BIN C over the period (or set of periods), and so-on andso-forth. In embodiments, the migration probability information mayinclude a probability (or set of probabilities) for each mobile stationin the network. Alternatively, the migration probability information mayinclude probabilities for sub-sets of mobile stations (e.g., mobilestations near the cell-edge, mobile stations having high velocities,mobile stations in high-traffic bins, etc.). Next, the method 300proceeds to step 350, where the network device estimates resourceavailability based on the estimated migration probability information.Thereafter, the method 300 proceeds to step 360, where the networkdevice provisions network resources based on the estimated resourceavailability information. Different steps of the method 300 can beperformed by different devices/entities. For example, a central entitymay perform some steps, while a distributed entity may perform othersteps. Some steps may be performed in parallel by multiple distributedentities.

Aspects of this disclosure may allow a centralized entity (e.g., a 3rdparty or a remote entity) to perform admission control without impactingquality of service (QoS) guarantees when users are migrating from onecell to another. In one example, resource usage expectations may beevaluated in future times to: obtain resource cost at each location binsas a function of loading of the neighboring links (e.g., preparedoffline); obtain the probability that the each user might be in bin k intime T with the knowledge of past history or, in the absence of thesame, using a mobility model); obtain the assignment probability foreach location bin to a given access node for each time bin at differentloading combinations; use increasing threshold margins for differenttime bins as uncertainty of prediction increases. Other aspects of thisdisclosure include guard band evaluation based on assignment probabilityand location which includes: using the current loading level andlocation of users and find the cost of each path and decideadmissibility. A guard band may be used according to the expectedtraffic to a given cell using expected probability considering thedecrease in uncertainty of this expectation for closer time bins.

Embodiment techniques may be performed, or otherwise facilitated, bycentral entities, such as a telecommunications service provider (TCSP).FIG. 4 illustrates a network 400 in which a mobile station 450 interactswith a content provider 490. The network 400 includes central entities460, 470 configured to interact with NTOs 410, 420, 430 to achievemobility prediction for the mobile station 450. As shown, the NTOs 410operates a core network 401, and the NTOs 420, 430 operate accessnetworks 402, 403. The core network 401 may be any type of networkcapable of interconnecting the access networks 402, 403 with a contentprovider 490. In some embodiments, the access network 402 corresponds toa wireless local area network (WLAN) serviced by a Wi-Fi access point(AP), and the access network 403 corresponds to a radio access network(RAN) serviced by one or more network APs, e.g., macro base stations(BSs), pico BSs, relays, etc. Interactions between the core network 401and the access networks 402, 403 may be handled by edge routers 412,413.

In some embodiments, the central entities 460, 470 may coordinatemobility prediction between the networks 401-403. In one example, thecentral entity 470 may facilitate mobility prediction between regionsassigned to APs of the access network 402, 403. For instance, thecentral entity 470 may retrieve statistical mobility information fromthe NTOs 420, 430, and compute a migration probability table that allowsmobility prediction to be performed in real-time based on mobilityparameters of a given mobile station or set of mobile stations. Thecentral entity 470 may perform the mobility prediction directly bycollecting dynamic mobility information and returning allocationinstructions and/or mobility prediction results. Alternatively, thecentral entity 470 may distribute the migration probability table to theNTOs so that mobility prediction may be performed locally.

FIG. 4 illustrates an embodiment network 400 in which a central networkentity 470 provides connectivity services. More specifically, thecentral network entity 470 considers user mobility in the admissionprocess such that admission can be performed with greater accuracy. Foreach bin, a load/utility based cost function is provided to a controllerbeforehand (e.g., using offline evaluations) for different link loadvalues (e.g., serving cell, neighbor cell, etc.) and different servicetypes. Links inform the current loading/utility and variation in regularintervals. Controller decides the admission and/or path (minimum costpath) given user requirement. Load may be quantized (for databaseentries), and can include neighbor's traffic density distribution.

Aspects of this disclosure provide techniques for estimating resourceavailability based on mobility prediction. FIG. 5A illustrates a method500 for estimating resource availability based on mobility prediction,as might be performed by a network device. As shown, the method 500begins with step 510, where the network device determines a firstprobability that user (u) migrates to new bin during interim periodbetween first time interval and second time interval.

Next, the method 500 proceeds to step 520, where the network devicedetermines a second probability that user's session remains establisheduntil second time interval. Thereafter, the method 500 proceeds to step530, where the network device estimates resources required by user'ssession in the new bin. In some embodiments, this estimate may alsoinclude an indirect resource cost corresponding to a decreased spectralefficiency of neighboring links as a result of interference associatedwith the user's session. In some embodiments, the new bin may beserviced by a single AP, in which case there is a 100 percentprobability that the user's session will be assigned to that AP. Inother embodiments, the new bin may be serviced by a multiple APs, witheach AP having an assignment probability for the bin, e.g., APi isassigned 50 percent of service flows in bin, AP₂ is assigned 30 percentof service flows in bin, and AP₃ is assigned 20 percent of service flowsin bin. In this case, different resources requirements may be associatedwith different APs based on, inter alia, path loss between the bin andthe corresponding AP. Moreover, the respective resource requirements mayneed to be weighted based on the assignment probability corresponding tothe AP. In some embodiments, the total estimated resource required maybe the sum of the weighted resource requirements for the multiple APs.Subsequently, the method 500 proceeds to step 540, where the networkdevice estimates the resource usage for the second time interval inaccordance with first probability, second probability, and estimatedresources required by user's session. FIG. 5B illustrates an embodimenttechnique for estimating the resource usage. As shown, this techniquemultiplies the first probability, second probability, and the requiredresources to obtain an estimated resource usage.

Load based cost schemes disclosed herein may be more accurate than otherschemes, as once a load is fixed, the variation of resource cost issmall (e.g., aggressive service provisioning schemes can be developed).FIG. 6 illustrates a protocol diagram for an embodiment communicationssequence 600 between a central entity and one or more NTOs. As shown,the communications sequence 600 begins with step 610, where the NTOscommunicate statistical information to the CE. In some embodiments, thestatistical information includes bin-based statistical informationrelating to loading and/or traffic patterns in networks operated by theNTOs. The loading and/or traffic patterns may be obtained fromreal-world data or simulations, and may indicate service sessionparameters (real, simulated, or otherwise) for a corresponding coveragearea. For example, the loading information may indicate, inter alia,path loss information in various bins of a coverage area, and thetraffic pattern information may indicate a mobility parameters andpathways/trajectories of a real or simulated service sessions.

Next, the communications sequence 600 proceeds to step 620, where the CEcomputes a migration probability table based on the statisticalinformation provided by the NTOs. The migration probability table mayspecify migration probabilities based on, inter alia, the bin-locationand mobility parameters of a given mobile station. In one example, themobility parameters include a velocity parameter. In some embodiments,the migration probabilities may be associated with multiple fixedperiods. For example, the migration probability may indicate that amobile station traveling at given velocity has a first probability ofmigrating from bin A to bin B after a first period (e.g., after onetime-slot), a second probability of migrating from bin A to bin B aftera second period (e.g., after two time-slots), and so-on and so-forth.

Thereafter, the communications sequence 600 proceeds to step 630, wherethe NTOs report dynamic updates to the central entity. The dynamicupdates may include various real-time network parameters, such asloading, traffic patterns, latency, interference, and/or other relevantnetwork information. Subsequently, the communications sequence 600proceeds to step 640, where the central entity evaluates the currentnetworks conditions in accordance with the dynamic updates provided bythe NTOs. Thereafter, the communications sequence 600 proceeds to step650, where the central entity makes admission and path selection for thenetwork based on the migration probability table, the current networkcondition evaluation, and new service requests received by the NTOs.

In some embodiments, the current network condition evaluation mayproduce an internal loading image. This internal loading image may begenerated via simulation, and may be tracked and updated with real data.Additionally, the current network condition evaluation may utilize acost function to estimate resources required to sustain (or establish) aservice flow in a bin location. The cost function can be modified to beaggressive or conservative based on network conditions (e.g., a delta).For simulations, loading in different cells may be varied to obtainresource cost function (RCF), and each potential link may have its owncost vs. load function. Some admission control schemes may assume or usea ‘Resource Guard Band’ for mobility consideration. Embodiment admissioncontrol schemes of this disclosure take the impact of mobility onresource usage into consideration, in addition to evaluating therequired guard band. Some embodiments may use the same Resource CostFunction as described in U.S. Provisional Patent Application 61/737,551,which is incorporated herein by reference as if reproduced in itsentirety. Some embodiments may require the probability that a user isassociated with a given bin assigned to a given cell. This is assumed tobe obtained from operator of each link, e.g. network owner/operator(NTO), based on the historical data.

Aspects of this disclosure provide techniques for estimating resourceutilization/availability based on mobility parameters of mobilestations. FIG. 7 illustrates a diagram 701 of estimated service sessiontrajectories and diagrams 702, 703 of corresponding resource utilizationprojections, which may be obtained using embodiment techniques providedherein. As shown, the diagram 701 depicts projected trajectories for afirst user equipment (UE1) and a second user equipment (UE2). Thetrajectories begin at a current time (t=o), and extend over three futuretimes (t=1, t=2, and t=3). The diagrams 702, 703 depict estimatedresource usage for the current time and the three future times for twocells depicted in the diagram 701.

Aspects of this disclosure provide techniques for evaluating guard bandsbased using the migration probabilities. In one example, additionalresource usage for users moving from one cell to the other is determinedin accordance with an arrival rate of the new services. Thereafter, arequired guard band is determined. In another embodiment, mobility isdetermined in accordance with an expected resource usage by handovercalls. In this scheme, time is divided into time bins. The algorithmevaluates the expected usage of resources in cell j, at time bin t,using the following information: The probability a user can be at agiven bin k in time t (can be designed using a generic mobilityprediction method knowing current location and the speed. More accuratehistorical data based model could also be used.); the probability that auser at bin k be assigned to cell j (this is also new information thatmay be obtained using historical data. This may be a function of routingand scheduling scheme as well as loading. An approximation may beassumed as fixed once loading is known.); and the resource usage of theuser when it is in bin k (similar to that described above).

With respect to an embodiment method based on expected resource usage.An area is divided into bins. Equal number of bins with sufficientgranularity. Regions with equal geometry are assigned a bin. Moreappropriate for these two cases: For uplink, and geometry is evaluatedfor a fixed IOT. For downlink when the power is fixed this can be used.For downlink when power is not fixed, average geometry taking differentpower combinations have to be used. For each bin, estimated resourceneed R_(est) from a given cell is evaluated using the probability ofuser being at bin k at time t, resources required for that service atbin k, and the assignment probability at bin k to cell i for a givenservice type under the given load matrix as shown below. R_(est)(t, i,u)=Σ_(u in cell i) Σ_(k in cell i)P_(m)(t, k, u), (1−P_(d)(t, u)).R_(r)(k, L(t), S(u)). P_(a)(k, L(t,), S(uu)), where R_(est) (t, i, u) isthe expected resource usage by the user u in cell i at time t,P_(d)(t,u) is the probability that a user will be departed before a timeperiod (t) has elapsed, P_(m) is the probability of user u moving to bink at time t, and P_(a) are the resources required and assignmentprobability (to cell i) respectively, at bin k when loading vector(including neighbor load) L(t) s for service class of the user u.

Notably, Pa and Rr could be evaluated based on past data or off-lineonline simulations/ emulations. Further, “All k in cell i” may mean thatall bins having non-zero assignments probability to cell i. Pm dependson the mobility model. Further, “All u in cell i” may mean that all UEshaving a non-zero probability to move to “All bins in cell ” in time t.

With respect to another embodiment method based on expected resourceusage. Now admission control is done for this service as below. Thetotal estimated resource usage for all the users at time t for cell I iscomputed, and then it is determined whether the following is satisfiedfor all t, t<t max to admit the call. The following equations may beused to compute expected resource usage: R_(totale) _(_) _(est)(t,i)=Σ_(u in cell i)R_(est)(t, i, u); f_(cost)(R_(total) _(_) _(est)(t,i))<T_(cost)−uM(t), where uM(t) is the cost margin kept for uncertaintyof the expected cost over time, T_(cost) is the cost threshold to beused for admission, and T_(cost) (. is the cost function used by theresource owner based on the total resource usage.

In some embodiments, “cost guard band” depends on how much loading wouldmove to this cell in the near future from the neighboring cells and howmuch load is moved out to neighbor cells from this cell. It may bepossible to apply weights to some of the gap(s) using the followingequation: Σ_(t=1) ^(tmax)W(i). (T_(cost)−uM(t)−f_(cost)(R_(total) _(_)_(est)(t, i))>0.

With respect to an embodiment guard band method. In this approach, it isevaluated whether the expected amount of resources that is needed withinsome time to cater for the handed in sessions from the neighbors. Theevaluation is performed in accordance with the following: B_(est) (t,i)=Σ_(all j in neighbour list) Σ_(u in cell j) R_(cst)(t, i, u), whereinB_(est)(t, i) is the total expected resource usage in cell i at time tby the users in all the neighbor cells.

Now the expected load reductions due to handoffs from cell i to othercells are removed in accordance with the following:

B _(add)(t, i)=B _(est)(t, i)−Σ_(all j in neighbour list)Σ_(u in cell i)R_(cst)(t, j, u)

Now the additional bandwidth is weighed to give immediate bandwidth ahigher weight. This is done in accordance with the following: B₀=Σ_(t=1)^(T)W(t−t₀,)*B_(add)(t, i)).

Finally, a cost margin is applied. The cost margin is to be used as aguard band which depends on the average arrival rates in cell i fordifferent service types A(i, s(u)). Applying different margins todifferent services may avoid excessive call drop outs for handovers. Thecost margin is applied in accordance with the following:ΔT(s(u))=f_(cost) _(margin) (A(i, s(u)), B₀).

Thereafter, to admit a call in cell j, it may be useful to apply themargin on top of previous admission control algorithm provisional filingHW 81086298US01 as may be performed in accordance with the following:f_(cost) (additonal resource)<T_(cost)−ΔT(s(u))

Aspects of this disclosure are relevant to an admission control methodwhich takes mobility, and include: areas are divided into bins;associated with each bin is a probability that a service type S isserved by a given cell (using historical data, or geometry); theexpected resource usage of a mobile at different time slots areevaluated using the expected probability of the user in a given bin atthat time slot and the assignment probability of that service at thatbin; the cost is estimated based on a cost function for this resourcesusage; a cost will be estimated for the call for all the other cells; ifthe cost is lower than a threshold admit the call. The requiredbandwidth does not depend on the additional bandwidth required. It isthe handoff rate, a handover rate and new call rate (weighted by Refwhich needs bandwidth reservation.)

Additional aspects of this disclosure include assignment probability,bin to bin transition probability, departure expectation, and expectedresources for future times. Benefits provided by this disclosure includeproviding a variety of virtual network services when control links areslow; routing and cost based admission may not differ greatly fromoptimal routing scenarios.

The following references are related to subject matter of the presentapplication. Each of these references is incorporated herein byreference in its entirety: Wikipedia, “Asynchronous TransferMode”<http://en.wikipedia.org/wiki/Asynchronous Transfer Mode>;Wikipedia, “Open Shortest Path First” <http://en.wikipedia.org/wiki/OpenShortest Path First>; and Mostafa Zaman Chowdhurya, Yeong Min Janga, andZygmunt J. Haasb, Department of Electronics Engineering, KookminUniversity, Korea “Call Admission Control based on Adaptive BandwidthAllocation for Multi-Class Services in Wireless Networks”, WirelessNetworks Lab, Cornell University, Ithaca, N.Y., 14853, U.S.A

Aspects of this disclosure allow multiple migration probabilities to beconsidered when estimating resource utilization/availability. In someembodiments, migration probabilities can be predicted based without anyvelocity information. FIG. 8 illustrates an embodiment for estimatingthe location of a user (starting in cell 1) over the next seven timeperiods with no velocity information being known for the user. As shown,there is a high probability (e.g., 99%) that the user will remain incell-i for the next five timeslots. Thereafter, the user has a smallprobability (e.g., 2%) of migrating to any of the neighboring cellsduring the sixth timeslot, and a slightly larger probability (e.g., 3%)of migrating to any of the neighboring cells during the seventhtimeslot. Hence, the user's likelihood of migrating to a different cellincreases over time, even when no velocity information is known.

Aspects of this disclosure provide techniques for adjusting handovermargins based on mobility predictions. FIG. 9 illustrates an embodimentmethod 900 for adjusting handover margins based on estimated migrationprobabilities, as may be performed by a network device. As shown, themethod 900 begins at step 910, where the network device obtains mobilityparameters corresponding to one or more mobile stations during aninitial time interval. The mobile stations may include any mobilestation in or near a coverage area. Next, the method 900 proceeds tostep 920, where the network device estimates migration probabilityinformation for the one or more mobile stations based on the mobilityparameters and a migration probability table. The migration probabilityinformation may include probabilities that the mobile stations willaccess the network from the coverage area at a future time intervals.For example, the migration probabilities may indicate a probability thateach mobile station currently inside the coverage area will remaininside the coverage area, as well as a probability that mobile stationsoutside the coverage area will migrate into the coverage area.Thereafter, the method 900 proceeds to step 930, where the networkdevice adjusts a handover margin of the coverage area for one or moresubsequent time intervals based on the estimated migration probabilityinformation.

FIG. 10 illustrates block diagrams of high level techniques forpredicting resource availability. The first technique includes combiningan effective bandwidth-driven traffic characterization, mobilityawareness, and wireless channel awareness. The second technique includescombining an effective bandwidth-driven traffic characterization andwireless channel awareness.

With respect to home traffic, aspects of this disclosure predict thespectral efficiency of an active UE at any given time according tooffline channel and mobility data, or online channel and mobility data,or both. Thereafter, the resources needed for QoS support may becomputed based on effective bandwidth.

With respect to handover traffic, aspects of this disclosure compute theresources reserved for QoS support for handover traffic based onhistorical data using effective bandwidth. Thereafter, the reservationpattern is adjusted based on online data (e.g., UE shadow clustering).The remaining resources can be computed by subtracting the resourcesneeded for home traffic and handover traffic and guard bands, if any.FIG. 11 illustrates an embodiment system for predicting resourceavailability.

Aspects of this disclosure predict the amount of amount of availableresources via effective bandwidth with traffic multiplexing gain forservice admission in wireless networks. FIG. 12 illustrates anotherembodiment system for predicting resource availability.

Aspects of this disclosure allow resource requirements of mobile stationto be estimated as a function of projected migration trajectory. Morespecifically, a mobile stations migration trajectory can be predictedbased on mobility parameters, and thereafter, the path loss for eachpoint on the trajectory can be estimated using, inter alia, networkloading information. FIG. 13 illustrates a diagram for projectingdifferent resource requirements along a projected trajectory of a mobilestation. As shown, the projected resource requirement of the mobilestation changes based on the spectral efficiency of each point along theestimated trajectory.

In an embodiment, an access point logs or sends the followinginformation to a centralized entity: the traffic information of each HOtraffic flow; the service holding time of this HO flow when it'sserviced by this access point; the movement of each HO traffic flow.

This traffic information is then converted to an effective bandwidtheither locally or remotely at a central entity. Thereafter, a spectralefficiency is retrieved from a database for those locations experiencedby the UEs. After the fact, this access point or the central entity cancompute an actual amount of resources needed for this HO traffic flow.Computing the actual amount of resources may include summing up all theresources needed for HO traffic flows at any time window and store thisinformation in the database. Given a particular network configuration,it may be desirable to average as many samples as possible in thedatabase. This offline data is used to compute a reference resourcereservation curve for HO traffic. FIG. 14 illustrates a diagram ofanother embodiment system for predicting resource availability.

Aspects of this disclosure may compute semi-static or dynamic resourcereservation for HO traffic in accordance with offline historical data,which saves online signalling overhead. No information exchange amongaccess nodes is needed.

FIGS. 15-20 illustrate simulation results using a simulations setupoutlined as follows: Topology=100 m×100 m; 1 cell (with×amount ofresources in Hz); Number of stationary UEs=1000 (all served by a celllocated at (0,0)); Spectral efficiency=1/distancê2; Number ofsessions=1000; Session duration=random variable {1,1000}; Idleperiod=random variable {1,100}; Traffic source=ON-OFF traffic (alpha,beta, R)=(2,3,1000 b/s); Rate of each sessions are Peak=1000b/s,Mean=400b/s, and Effective bandwidth=612.5b/s with 1% outage probability(due to buffer overflow exceeding 40 cells); UE scheduling=random order;simulation time=1,000 seconds (iseed). Schemes considered include Peak,Mean, Standard effective bandwidth (r_eff), and Effective bandwidth withmultiplexing gain (r_eff*).

FIG. 21 illustrates a method 2100 for computing resources needed forhandover traffic, as may be performed by a network device. As shown, themethod 2100 begins at step 2110, where the network device identifiestraffic that is likely to be handed over from other nodes, and estimatesthe effective bandwidth of that traffic. Next, the method 2100 proceedsto step 2120, where the network device sums up the effective bandwidthsand stores them in a database. Thereafter, the method 2100 proceeds tostep 2130, where the network device evaluates a desired spectralefficiency around its cell edge area. Subsequently, the method 2100proceeds to step 2140, where the network device picks an average HOoutput rate and computes the corresponding data rate for handling thehandovers. Thereafter, the method 2100 proceeds to step 2150, where thenetwork device computes an amount of resources needed for the handovertraffic based on the selected data rate and the spectral efficiency.

Other aspects of this disclosure adjust the reference resourcereservation pattern curve using real-time system measurements asfollows: for each time window, compute the difference between anexpected amount of resources needed using on offline data and an actualamount of resources needed for HO traffic; the actual amount ofresources reserved for HO traffic is a function of an expected amount ofresources needed using on offline data and the previous resourcedifferences for HO traffic.

FIG. 22 illustrates a method 2200 for updating a parameter of a spectralefficiency prediction function based on resulting spectral efficiencydata fed back from the network, as may be performed by a network device.As shown, the method 2200 begins at step 2210, where the network devicemonitors spectral efficiency of a UE's service session. Next, the method2200 proceeds to step 2220, where the network device estimates a ratedemand of the UE's service session at a future instance in time. Theestimation may be based on traffic characteristics of the UE's servicesession, historical rate demands, or other parameters. Next, the method2200 proceeds to step 2230, where the network device applies aprediction function to generate an estimated spectral efficiency of theUE's service session at a future instance in time. The function mayinclude variables corresponding to historical and/or dynamic networkdata, and may account for migration prediction. In one example, thefunction estimates the UE's future location, and then projects a pathloss for that location based on, for example, historical path loss,instantaneous data loads, projected network loading, current/futurenetwork configurations, etc. Thereafter, the method 2200 proceeds tostep 2240, where the network device estimates the resource requirementof the UE's service session at the future instance in time. Thisestimation may be based on the estimated rate demand and the estimatedspectral efficiency.

Thereafter, the method 2200 proceeds to step 2250, where the networkdevice determines whether spectral efficiency correction is needed. Thismay include determining whether a difference between an actual spectralefficiency and the estimated spectral efficiency exceeds a threshold. Ifspectral efficiency correction is needed, the method 2200 proceeds tostep 2260, where the network device updates a parameter of theprediction function to correct the error in the estimated spectralefficiency. The parameter may correspond to any one of a variety ofcomponents, such as the path-loss associated with a BIN, overall networkinterference, etc. After spectral efficiency correction is completed (orif it is not needed in the first place), the method 2200 reverts back tostep 2210. In some embodiments, the prediction function and parameteradjustments may be applied iteratively until the estimated spectralefficiency falls within an accuracy range. In the same or otherembodiments, the spectral efficiency may be estimated more frequentlythan rate demand (e.g., rate demand may be updated semi-statically).

Aspects of this disclosure provide the following rationale. If theactual amount of resources needed for HO traffic is below a referencecurve in the past time windows, it is likely that the amount ofresources needed in the near future will also be below a referencecurve, since some of those HO traffic flows will sustain their servicesfor a while. The same goes to the case where the actual amount ofresources needed for HO traffic is above a reference curve in the pasttime windows.

FIGS. 23 and 24 illustrate simulation results using a simulations setupoutlined as follows: Topology=100 m×100 m; Center cell=a circle ofradius 25 m at origin (o,o); Spectral efficiency=1/distancê2; UEmobility model=random waypoint; Effective bandwidth of eachsession=random variable; Session duration=random variable; Idleperiod=random variable; Simulation time=10000 seconds; Number ofUEs=1000; Number of active UEs={200, 700, 1000, 200} for {[0 2500] [25015000] [5001 7500] [7501 1000]}seconds

In the context of handover margins, the number of available resources(e.g., resources available for allocation) increases as the number ofresources reserved for bandwidth traffic decrease. FIG. 25 illustrates adiagram of a bandwidth spectrum that is divided into available resourcesand resource reserved for handover traffic. Some schemes may staticallyreserve a certain amount of bandwidth for handover calls. Such schemesmay assume that UE mobility is random and unpredictable, and may requireno communication required among cells, thereby allowing the scheme to beeasily implemented. However, such schemes may over-reserve resources andlead to higher than necessary call-blocking.

Other schemes may rely on shadow clustering, which identifies a set ofcells that a particular UE will likely traverse. Depending on themovement characteristics of a UE, different cells might have differentweights since some cells are more likely to be crossed than the others.Loading information is exchanged. FIG. 26 illustrates a diagramdemonstrating how loading data is exchanged in a network that utilizesshadow clustering. Such schemes can be improved through mobilitypredictions. FIG. 27 illustrates a diagram showing how a mobilestation's velocity parameter at an instance time (T) can be used topredict possible migration positions at a future time (T+Δt).

In some embodiments, mobility prediction may account for variations inhistorical migrations over periodic or recurring periods. For instance,mobility parameters (e.g., velocity+BIN) may predict a differentmigration trajectory in the morning than in the evening, due to temporalfluctuations in historical migration patterns. By way of example, motorvehicles traveling over roadways may take different routes or paths inthe morning than in the evening due to, inter alia, traffic congestion.In some embodiments, migration probability tables may associate specificmigration patterns with specific user profiles to leverage the relativepredictability of an individual user's schedule (e.g., John Doe may haveestablished morning/evening commutes on workdays). FIGS. 28A-28B showhow specific migration patterns of an individual user can be modeled ina migration probability table.

Mobility prediction techniques may allow for new resource allocationtechniques to be implemented. FIG. 29 illustrates a table showing newallocation techniques for different traffic and prediction scenarios.FIG. 30 illustrates a state diagram for adjusting prediction functionparameters based on effective bandwidth. FIG. 31 illustrates a graph ofbandwidth aggregation. FIG. 32 illustrates a graph of outageprobabilities for different numbers of sources. FIG. 33 illustrates agraph of allocated rates for different numbers of sources.

Aspects of this disclosure provide techniques for predicting migrationprobabilities amongst geographic areas of a wireless network. In someembodiments, geographical areas may be further divided into a number ofbins such a position of an object can be more precisely identified. Forexample, the accuracy of a GPS (e.g., a Garmin GPS, etc.) may beapproximately 98.9% within a circular area having a 2.55m radius. Binsize can be determined in various ways. In one embodiment, an area zoneper each segment of service duration is identified for a threshold levelof mobility prediction accuracy. In another embodiment, a remainingsystem capacity for each (bin, time) tuple can be identified. Theremaining system capacity may depend on the granularity of information,e.g., per bin per access node (physical or logical), per zone, peraccess node, per access node, per bin, per zone, etc. FIG. 34illustrates a network configured for per-bin geographical areapartitioning. FIG. 35 illustrates a network configured for per-zonegeographical area partitioning. Evaluation of the remaining systemcapacity may depend on the accuracy of mobility prediction.

In some embodiments, the remaining “system capacity” can be evaluated ona per-bin-per-access-node basis, e.g., considering physical or logicalcell association such as CoMP cell association. FIG. 36 illustrates agraph of a simulated system capacity evaluation on a per-bin-per-accessnode basis. Δt any given time, it may be possible to have knowledge ofthe “throughput” reserved or the number of resources reserved if SE isfixed for admitted users at any access node. One way to compute theremaining system capacity per bin is to fix the spectral efficiency(e.g., 95 percentile) with the number of available resources. Ideally,this would produce a precise spectral efficiency per bin, but it may notbe available in the database. Instead, it may be useful to have acumulative distribution function (CDF) of spectral efficiency per bin.This location-dependent spectral efficiency could be averaged overdifferent access node associations, different numbers of UEs per accessnode, different configuration (e.g., PC, scheduling, IoT levels),different applications, or different times of day. Since the wirelesschannels are generally not independent from one time to another, itwould be useful to have an autocorrelation function of spectralefficiencies or remaining throughput. This autocorrelation function maybe used to predict the service outage probability when the duration of arequested service is short or when an admitted service is already insession.

Estimating resource usage per-bin-per-access node may be relativelyaccurate when UEs do not move or when there is relatively accuratemobility prediction, i.e., a vector of (location, time). In such cases,the admission control logic may check if the remaining capacity (e.g.,target SE x available RB) at that UE location is larger than therequired effective bandwidth. One potential downside to this scheme maybe that the effectiveness of service admission is highly contingent onthe accuracy of mobility prediction.

Another scheme may compute remaining “system capacity” on aper-zone-per-access-node basis (e.g., considering physical or CoMP cellassociation), which may offer reduced complexity when compared to otherschemes as well as offer improved performance when mobility predictionis less accurate/timely. FIG. 37 illustrates a graph of a simulatedsystem capacity evaluation on a per-zone-per-access-node basis. Once theremaining “system capacity” per bin per access node is known, it ispossible to group several bins into a zone (e.g., of same or differentsizes) based on UE mobility. In essence, this scheme enhances theaccuracy of UE mobility prediction and allows the remaining systemcapacity to be evaluated for each zone per access node. This scheme maybe beneficial when a UE exhibits rapid movement so long as mobilityprediction is accurate within the coverage of each access node. Theadmission control logic may check if the remaining capacity per zone islarger than the required effective bandwidth prior to admitting aservice request.

In another approach, remaining “system capacity” can be evaluated on aper access node basis (e.g., considering physical or CoMP cellassociation). FIG. 38 illustrates a graph of a simulated system capacityevaluation on a per-access node basis. This may further reducecomplexity. Once the remaining “system capacity” per bin per access nodeis obtained, it is possible to compute the remaining “system capacity”per access node. This scheme may offer good performance as long as thereis accurate mobility prediction in terms of which access node(s) a UEwill be connected to at any given time. The admission control unit maycheck if the remaining capacity is larger than the required effectivebandwidth.

Since the system capacity is evaluated for the coverage area of anaccess node, the spectral efficiency or throughput CDF curves can bewidespread, which can easily lead to conservative admission control.Thus, after admission, the system may continuously monitor the resourceusage of the corresponding service and the mobility information of thecorresponding UE.

Another scheme may compute remaining “system capacity” on a per-bin orper-zone basis (e.g., ignoring cell association) to achieve furthercomplexity reduction. FIG. 39 illustrates a graph of a simulated systemcapacity evaluation on a per-bin basis. Once the remaining “systemcapacity” per bin is obtained, several bins can be grouped into onezone, and different zones can be of different sizes based on UEmobility. In the physical network, it is possible to obtain a CDF ofremaining system capacity (i.e., available throughput) per each bin.When multiple SEs are available (e.g., with different cellassociations), the CDF can be obtained by finding the maximum throughputavailable, e.g., max{SE₁×RB₁, SE₂×RB₂, . . . , SEn×RBn,SE_comp₁×RB_comp₁, . . . SE_compm×RB_compm}. Thus, in the end, it ispossible to map end-to-end available throughput such that the admissioncontrol unit may check whether the remaining capacity is larger than therequired effective bandwidth. This approach essentially decouples thetask of service admission from the task of resource allocation, and theadmission control unit determines if the system can support the service,while a NETWORK-layer and MAC-layer resource manager determines howresources should be allocated. The resource allocation pattern can bereported back to the admission control to keep a record of how resourcesshould be reserved and how remaining system capacity should becalculated for the next coming service request. Or, the admissioncontrol can also assume a certain resource allocation algorithm, e.g.,load balancing would be done via routing, equal resource allocation,shortest path, WiFi-first, lowest cost, etc.

Aspects of this disclosure allow system capacity to be calculated formulti-path routing. When multi-path routing is enabled, differentpackets can be sent via different links simultaneously, e.g., odd(even-)-numbered packets are routed through access node 1 (access node2). To compute the maximum available capacity at a particular locationassuming single-hop links, an optimization problem can be formulated asfollows: (1) minimize {Σ_(m=1) ^(M)x_(m)}; (2) Σ_(m=1) ^(M)x_(m)≦B; (3)0≦x_(m)≦b_(m), ∀m; (4) Σ_(m=1) ^(M)SE_(m)x_(m)≧γ, where B is the totalnumber of resources (e.g., RBs) that can be used for packettransmission, b_(m) is the available number of resources at access nodem, SE_(n), is the target SE of access node m, y is the effectivebandwidth of this UE, and x_(m) is the decision variable of the amountof resources access node m should allocate to this UE. The objectivefunction may be used to minimize the total amount of wasted resources.This problem may be simplified as follows: (1) Set x_(m)=0 for all m;(2) Find the access node m* where SE_(m)* is the maximum; there is noaccess node in the list, stop; (3) Check if there is any resourceavailable at access node m*, if yes, then set x_(m)*=x_(m)*+1, if no,remove access node m* and revert to step two; (4) Check if the requiredeffective bandwidth is satisfied, if satisfied, stop, if not satisfied,go back to step two.

A load-aware algorithm can be given as follows: (1) Set x_(m)=0 for allm, and add all the potential access nodes to the list; (2) Find theaccess node m*where b_(m)* (or SE_(m)*b_(m)*) is the maximum; if thereis no access node in the list, stop; (3) Check if there is any resourceavailable at access node m*, i.e., x_(m)*<b_(m)*, if yes, setx_(m)*=x_(m)*+1, if no, remove access node m* from the list and go backto step two; (4)check if the required effective bandwidth is satisfied,if satisfied, stop, if not satisfied, go back to step two.

Aspects of this disclosure allow for the calculation of system forrandom medium access control such as 802.11, where the system capacityprimarily depends on the MAC protocol and how many users compete for theresources in a distributed manner. One way to compute a remaining systemcapacity is given as follows: (1) Obtain a data rate available perlocation from past historical data; (2) Obtain the number of STAsconnecting to each access node; (3) Remaining system capacity of anaccess node=f(data rate, overhead, number of STAs, MAC protocol,priority). In random access MAC, adding a new traffic source in thesystem will decrease a resource share of each admitted source in thesystem. Besides computing the system capacity available for the incominguser, it may also be helpful to evaluate if the QoS requirements of thealready admitted users can be met by adding this new user. Aspects ofthis disclosure allow for usage of adaptive area partitioning.Generally, mobility prediction and UE behavior prediction may have somedegree of inaccuracy, so it may be inadvisable to reserve resourcesbeyond the access nodes currently serving a UE (unless we have 100%accuracy on mobility prediction and UE behavior prediction). Thus, thehome cell currently serving a UE tries to perform area partitioningbased on mobility prediction and reserves resources accordingly. Whenthis UE is handed over to another cell, this cell would try to performarea partitioning based on mobility prediction and reserves resourcesaccordingly.

Aspects of this disclosure provide adaptive guard band techniques thatutilize migration probability predictions. FIG. 40 illustrates anapparatus configured to compute handover margins (or guard bands). Inembodiments, neighboring access nodes may exchange long-term statisticsand/or instantaneous information to facilitate mobility prediction. Thisinformation may include probabilities that a UE of a certain trafficclass will go from access node A to access node B. The followingequation can be used to compute handover margins at access node k attime t: θ_(k)=Σ_(m≠k)a_(mk)(t) Σ_(j=1) ^(j)r_(m,j)^(eff)N_(m,j)(t)P_(mk,j)(t), where P_(mk,j)(t) is the probability that aUE of traffic class j moves from access node m to access node k at timet, N_(m,j)(t) is the “number of UEs” (e.g., average, estimated,instantaneous) of traffic class j in access node m at time t, r_(m,j)^(eff) is the estimated effective bandwidth of traffic class j in accessnode m, and a_(ink)(t) is the weighting factor for the aggregateeffective bandwidth for access node m to access node k at time t.

Another guard band design is described as follows: 1) dynamic guardbands are computed based on offline historical data, which saves a lotof online signalling overhead; and 2) no information exchange amongaccess nodes is needed for guard band computation. The basic steps ofthe proposed solution can be summarized as follows: For each access nodein each given time window, (1) Identify the traffic of each handover(HO) UE from other access nodes, and estimate the effective bandwidth ofthat traffic; (2) Sum up the effective bandwidths of all the HO UEs andstore them in a database; and (3) compute a guard band based on an HOoutage rate, an affordable data rate, an expected spectral efficiency,etc.

Referring back to FIG. 19, the guard band threshold(s) depicted can beused for UEs with “average” mobility (to be identified through trainingor background simulations). We should set different guard bandthresholds for different UE speeds as follows: βf (θ_(k)), where f (.)is a function mapping the throughput (bit/s) into the number ofresources (in Hz),

$0 \leq \beta \leq \frac{B}{f\left( \Theta_{k} \right)}$

and B is the total number of resources. So, when a UE sends a request toits home cell together with the speed profile of this UE (e.g., obtainedfrom a UE profile database) , the home cell computes the effective guardband and evaluates if it has enough resources to admit this service. Forexample, when a UE moves faster than an average UE, the amount ofresources needed for a home cell to serve this UE over certain durationis less. In other words, the “effective guard band” of this home cell isrelatively smaller, since this UE will soon be handed over to anothercell. In turn, the home cell is able to admit more services. FIG. 41depicts different guard band configurations for different levels of UEmobility.

The following steps can be used to compute effective guard bands: (1)Compute average UE speed at each access node [(a) Each access nodereports UE speeds to a database from time to time, (b) A mobility engineretrieves the UE speed information and computes an average UE speed foreach access node for a time window (e.g., morning, afternoon, evening),and reports the average speeds back to the database]; (2) Compute aneffective guard band for an incoming request [(a)The home cell requeststhe mobility information of this UE from a database storing UE mobilityprofiles, (b)The home cell retrieves its average UE speed from thedatabase storing the average UE speed at each access node]. FIG. 42illustrates an embodiment system for computing guard bands.

Aspects of this disclosure allow adaptive area partitioning techniquesand adaptive guard band adjustment techniques to be combined. FIG. 43illustrates a communications sequence for partitioning coverage areasand adjusting guard bands. The following is a description of thatsequence: (a) Each cell exchanges parameters (such as UE migrationprobabilities, loading, traffic characteristics, weights, etc.) that areused to compute a guard band. The guard band calculation can also beperformed by an NTO. (b) When a service request arrives, a serviceprovider will send a connectivity request to an NTO orconnectivity-as-a-service provider (CaaSP). (c) An NTO will try to lookup the mobility profile of this UE and exchanges the information withthe access nodes of interest, e.g., a home cell and its neighboringcells. (d) Should the mobility of this UE be somewhat predicted, thehome cell will perform geographical area partitioning according to theprovided UE mobility profile [(i) If the mobility and UE behavioral canbe perfectly predicted, each of the involved access nodes will performgeographical area partitioning and reserve resources accordingly, (ii)In the case of no mobility prediction, the step of geographical areapartitioning can be skipped]. (e) The home cell will determine its“effective guard band” based on the UE mobility information, iffeasible. If an effective guard band cannot be estimated, the home cellwill not modify its guard band. (f) With the amount of guard bandsdetermined, each cell computes the remaining system available per area(per access node) according to the decision of geographical areapartitioning. (g) The NTO will check if the system has enough capacityto satisfy the QoS requirements of this service request. (h) QoS andcontract negotiations can be triggered between the service provider andthe NTO. (i) An admission decision is issued to the service provider,and the corresponding service can be launched or declined.

After admitting UEs, operators may continue to monitor and estimate themovement of each UE. The rationale for this is that the amount ofresources needed for those admitted services can be computed moreaccurately. The following approach can be used to predict UE mobilitybased on an auto-regressive (AR) model: Set a duration of a trainingphase; During the training phase, a UE keeps a record of its (GPSlocation, time) entry at a regular or any pre-defined time interval; TheUE can either report its (GPS location, time) entry every time or reporta list of (GPS location, time) entries all at once at the end of thetraining phase to a mobility predictor; Δt the end of the trainingperiod, both the UE and the mobility predictor have the same sets of(GPS location, time) entries, and they both run the same mobilityprediction algorithm in parallel. One algorithm under consideration isbased on AR; Δt any time after the training period, if the differencebetween an actual (location, time) entry and an estimated (location,time) entry is less than a threshold, no reporting is necessary, and themobility predictor can assume that a predicted (location, time) isaccurate. Both the UE and the mobility predictor continue to run themobility prediction algorithm in parallel; If the difference between anactual (location, time) entry and an estimated (location, time) entry islarger than a threshold, a UE will report the actual (location, time)entry to the mobility predictor. Both the UE and the mobility predictoruse the updated inputs to continue to perform mobility prediction inparallel. FIG. 44 illustrates a protocol diagram of an embodimentcommunications sequence for achieving mobility prediction.

A simplified AR model for mobility prediction is as follows: {tilde over(z)}_(k)=c+Σ_(i=1) ^(p)φ_(i)z_(k−i), where p is the order of the ARmodel, z_(j) is the actual j^(th) observation, {tilde over (z)}_(k) isthe estimated k^(th) observation, φ_(i) l is the i^(th) coefficient ofthe AR model, and c is a constant. Denote the estimation errore_(k)=z_(k)−{tilde over (z)}_(k). A mobility predictor can be built asfollows: Initialize all the AR coefficients:

${\varnothing_{j} = \frac{w_{j}}{p}},$

for j=1,2, . . . . , p, where Σ_(i=1) ^(p)φ_(i)=1 and w_(b)≧0, f orj=1,2, . . . , p; Update the coefficients starting from j=1 to j=p:

${{\overset{\sim}{\varnothing}}_{j} = {\frac{1}{z_{k - j}}\left( {z_{k} - \frac{{je}_{k}}{p + 1} - {\sum_{i = 1}^{j - 1}{{\overset{\sim}{\varnothing}}_{i}z_{k - i}}} - {\sum_{i = {j + 1}}^{p}{\varnothing_{i}z_{k - i}}}} \right)}};$

Update the constant c: c=z_(k)−Σ_(i=p){tilde over (φ)}_(i)z_(k−i),

This predictor can be used to predict the speed and direction of a UE.Given two sets of xy-coordinates, it is possible to compute the speedand the direction as follows. Denote θ_(i) as the direction, s_(i) isthe speed at time slot I, and T is the duration spent travelling. Thus,

$\theta_{i} = \left\{ {{\begin{matrix}{\arctan \left( \frac{y_{i} - y_{i - 1}}{x_{i} - x_{i - 1}} \right)} & {{{if}\mspace{14mu} x_{i}} > x_{i - 1}} \\{{\arctan \left( \frac{y_{i} - y_{i - 1}}{x_{i} - x_{i - 1}} \right)} + \pi} & {{{if}\mspace{14mu} x_{i}} < x_{i - 1}} \\{\frac{1}{2}\pi} & {{{if}\mspace{14mu} x_{i}} = {{{x_{i - 1}\&}\mspace{14mu} y_{i}} > y_{i - 1}}} \\{{- \frac{1}{2}}\pi} & {{{if}\mspace{14mu} x_{i}} = {{{x_{i - 1}\&}\mspace{14mu} y_{i}} \leq y_{i - 1}}}\end{matrix}s_{i}} = \sqrt{\left( {x_{i} - x_{i - 1}} \right)^{2} + \left( {y_{i} - y_{i - 1}} \right)^{2}}} \right.$

It is then possible to estimate both the direction and the speed at timeslot and S_(i+1), respectively, using the proposed AR model. Notice thata standard continuous transformation is applied on the direction. Withthe estimates, xy-coordinates can be predicted as follows: {tilde over(x)}_(i+1)=x_(i)+{tilde over (s)}_(i+1)cos({tilde over (θ)}_(i+1));{tilde over (y)}_(i+1)=y_(i)+{tilde over (s)}_(i+1)sin({tilde over(θ)}_(i+1)).

Aspects of this disclosure allow adaptive guard bands to be predicted inreal-time (e.g., online, on-the-fly, etc.). This may be achieved byemploying the mobility information of the admitted active users tocompute guard bands at any given time t>o (in the future). Below is thebrief description of how to leverage the mobility information of theactive users: Assign a probability for each zone that an active UE couldbe located at time t>0; For each cell, compute average resources neededif UEs in other cells will hand over to this cell time t>0, anddetermine the amount of guard bands needed at time t>0; Repeat the stepsat the next pre-defined time. In order to determine that probability, asimilar AR mobility prediction model can be used. It is possible todetermine the probability for each zone that an active UE could belocated at time t based on the estimation error. Since the onlineapproach might be an over-estimate or an under-estimate, it may beuseful to combine the offline guard band calculation and the onlineguard band calculation. One way is to use a linear weighted average:Guard band at time t=alpha×online guard band at time t+(1-alpha)×offlineguard band at time t.

FIG. 45 illustrates a block diagram of an embodiment of a communicationsdevice 4500, which may be equivalent to one or more devices (e.g., UEs,NBs, etc.) discussed above. The communications device 4500 may include aprocessor 4504, a memory 4506, a cellular interface 4510, a supplementalinterface 4512, and a backhaul interface 4514, which may (or may not) bearranged as shown in FIG. 45. The processor 4504 may be any componentcapable of performing computations and/or other processing relatedtasks, and the memory 4506 may be any component capable of storingprogramming and/or instructions for the processor 4504. The cellularinterface 4510 may be any component or collection of components thatallows the communications device 4500 to communicate using a cellularsignal, and may be used to receive and/or transmit information over acellular connection of a cellular network. The supplemental interface4512 may be any component or collection of components that allows thecommunications device 4500 to communicate data or control informationvia a supplemental protocol. For instance, the supplemental interface4512 may be a non-cellular wireless interface for communicating inaccordance with a Wireless-Fidelity (Wi-Fi) or Bluetooth protocol.Alternatively, the supplemental interface 4512 may be a wirelineinterface. The backhaul interface 4514 may be optionally included in thecommunications device 4500, and may comprise any component or collectionof components that allows the communications device 4500 to communicatewith another device via a backhaul network.

FIG. 46 is a block diagram of a processing system that may be used forimplementing the devices and methods disclosed herein. Specific devicesmay utilize all of the components shown, or only a subset of thecomponents, and levels of integration may vary from device to device.Furthermore, a device may contain multiple instances of a component,such as multiple processing units, processors, memories, transmitters,receivers, etc. The processing system may comprise a processing unitequipped with one or more input/output devices, such as a speaker,microphone, mouse, touchscreen, keypad, keyboard, printer, display, andthe like. The processing unit may include a central processing unit(CPU), memory, a mass storage device, a video adapter, and an I/Ointerface connected to a bus.

The bus may be one or more of any type of several bus architecturesincluding a memory bus or memory controller, a peripheral bus, videobus, or the like. The CPU may comprise any type of electronic dataprocessor. The memory may comprise any type of system memory such asstatic random access memory (SRAM), dynamic random access memory (DRAM),synchronous DRAM (SDRAM), read-only memory (ROM), a combination thereof,or the like. In an embodiment, the memory may include ROM for use atboot-up, and DRAM for program and data storage for use while executingprograms.

The mass storage device may comprise any type of storage deviceconfigured to store data, programs, and other information and to makethe data, programs, and other information accessible via the bus. Themass storage device may comprise, for example, one or more of a solidstate drive, hard disk drive, a magnetic disk drive, an optical diskdrive, or the like.

The video adapter and the I/O interface provide interfaces to coupleexternal input and output devices to the processing unit. Asillustrated, examples of input and output devices include the displaycoupled to the video adapter and the mouse/keyboard/printer coupled tothe I/O interface. Other devices may be coupled to the processing unit,and additional or fewer interface cards may be utilized. For example, aserial interface card (not shown) may be used to provide a serialinterface for a printer.

The processing unit also includes one or more network interfaces, whichmay comprise wired links, such as an Ethernet cable or the like, and/orwireless links to access nodes or different networks. The networkinterface allows the processing unit to communicate with remote unitsvia the networks. For example, the network interface may providewireless communication via one or more transmitters/transmit antennasand one or more receivers/receive antennas. In an embodiment, theprocessing unit is coupled to a local-area network or a wide-areanetwork for data processing and communications with remote devices, suchas other processing units, the Internet, remote storage facilities, orthe like.

While this invention has been described with reference to illustrativeembodiments, this description is not intended to be construed in alimiting sense. For example, when there are cooperative transmissions,the percentage of traffic for each path need to be used when evaluatingthe resource cost for a given service. Various modifications andcombinations of the illustrative embodiments, as well as otherembodiments of the invention, will be apparent to persons skilled in theart upon reference to the description. It is therefore intended that theappended claims encompass any such modifications or embodiments.

What is claimed is:
 1. A method comprising: receiving, by a radio accessnetwork (RAN) device, a migration probability table from a networkcomponent in a core network, the migration probability table specifyingprobabilities that mobile stations will migrate into, out of, or betweenmultiple bins served by an access point based on mobility parameters ofthe mobile stations, each of the multiple bins being served the sameaccess point; and estimating, by the RAN device, an aggregate networkresource usage in the multiple bins during a future period based onmobility information of two or more mobile stations gathered during aprevious period; and admitting new user sessions to the access pointbased on the aggregate network resource usage in the multiple bins. 2.The method of claim 1, wherein the aggregate network resource usage isestimated based on the mobility information and a cell assignmentprobability for each bin.
 3. The method of claim 1, wherein theaggregate network resource usage is estimated based on the mobilityinformation and a resource usage for each bin as a function of neighborload cell.
 4. The method of claim 1, wherein the aggregate networkresource usage is estimated based on the mobility information and acurrent network load.
 5. The method of claim 1, wherein the migrationprobability table indicates a likelihood that a mobile stationassociated with a specified velocity will migrate between bins served bythe same access point before the end of the future period.
 6. The methodof claim 1, wherein estimating the aggregate network resource usage inthe multiple bins comprises: determining an expected resource usage fromthe multiple bins according to a current user distribution.
 7. Themethod of claim 1, wherein admitting new user sessions to the accesspoint based on the aggregate network resource usage in the multiple binscomprises: admitting a new user session in a bin if an estimated amountof available resources in the bin exceed an estimated network resourceusage for the new user session by more than a threshold.
 8. The methodof claim 1, wherein the migration probability table specifies a userprofile of an individual mobile station, and wherein the user profileindicates historical migration information that is unique to theindividual mobile station.
 9. A radio access network (RAN) devicecomprising: a processor; and a non-transitory computer readable storagemedium storing programming for execution by the processor, theprogramming including instructions to: receive a migration probabilitytable from a network component in a core network, the migrationprobability table specifying probabilities that mobile stations willmigrate into, out of, or between multiple bins served by an access pointbased on mobility parameters of the mobile stations, each of themultiple bins being served the same access point; and estimate anaggregate network resource usage in the multiple bins during a futureperiod based on mobility information of two or more mobile stationsgathered during a previous period; and admit new user sessions to theaccess point based on the aggregate network resource usage in themultiple bins.
 10. The RAN device of claim 9, wherein the aggregatenetwork resource usage is estimated based on the mobility informationand a cell assignment probability for each bin.
 11. The RAN device ofclaim 9, wherein the aggregate network resource usage is estimated basedon the mobility information and a resource usage for each bin as afunction of neighbor load cell.
 12. The RAN device of claim 9, whereinthe aggregate network resource usage is estimated based on the mobilityinformation and a current network load.
 13. The RAN device of claim 9,wherein the migration probability table indicates a likelihood that amobile station associated with a specified velocity will migrate betweenbins served by the same access point before the end of the futureperiod.
 14. The RAN device of claim 9, wherein the instructions toestimate the aggregate network resource usage in the multiple binsincludes instructions to: determine an expected resource usage from themultiple bins according to a current user distribution.
 15. The RANdevice of claim 9, wherein the instructions to admit new user sessionsto the access point based on the aggregate network resource usage in themultiple bins includes instructions to: admit a new user session in abin if an estimated amount of available resources in the bin exceed anestimated network resource usage for the new user session by more than athreshold.
 16. The RAN device of claim 9, wherein the migrationprobability table specifies a user profile of an individual mobilestation, and wherein the user profile indicates historical migrationinformation that is unique to the individual mobile station.
 17. The RANdevice of claim 9, wherein the migration probability table indicates alikelihood that a mobile station associated with a specified velocitywill migrate between bins served by the same access point before the endof the future period.
 18. A computer program product configured forimplementation in a radio access network (RAN) device, the computerprogram product comprising a non-transitory computer readable storagemedium storing programming, the programming including instructions to:receive a migration probability table from a network component in a corenetwork, the migration probability table specifying probabilities thatmobile stations will migrate into, out of, or between multiple binsserved by an access point based on mobility parameters of the mobilestations, each of the multiple bins being served the same access point;and estimate an aggregate network resource usage in the multiple binsduring a future period based on mobility information of two or moremobile stations gathered during a previous period; and admit new usersessions to the access point based on the aggregate network resourceusage in the multiple bins.
 19. The computer program product of claim18, wherein the aggregate network resource usage is estimated based onthe mobility information and a cell assignment probability for each bin.20. The computer program product of claim 18, wherein the aggregatenetwork resource usage is estimated based on the mobility informationand a resource usage for each bin as a function of neighbor load cell.